Presentation and description of two classification methods using data fusion based on sensor management

The application of multi-sensor fusion, which aims at recognizing a state among a set of hypotheses for object classification, is of major interest with regard to the performance improvement brought by the sensor complementarity. Nevertheless, this needs to take into account the most accurate information and take advantage of the statistical learning of the previous measurements acquired by sensors. When previous learning is not representative of real measurements provided by the sensors, the classical probabilistic fusion methods lack performance. The Dempster–Shafer theory is then introduced to face this disadvantage by integrating further information which is the context of the sensor acquisitions. In this paper, we propose a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as probability mass. These two methods are based on the theory of fuzzy events. Simulations of typical cases are developed in order to define the respective validity domains of these two methods. Afterwards, we are interested in the development of these two methods in the case where the previous learning is unavailable for an object and a global method of contextual information integration can be deduced.     

Enterprise Collaboration: On-Demand Information Exchange Using Enterprise Databases, Wireless Sensor Networks, and RFID Systems

New extended enterprise models such as supply chain integration and demand chain management require a new method of on-demand information exchange that extends the traditional results of a global database query. The new requirements stem from, first, the fact that the information exchange involves large numbers of enterprise databases that belong to a large number of independent organizations, and second, these databases are increasingly overlapping with real-time data sources such as wireless sensor networks and radio-frequency identification (RFID) systems. One example is the industrial push to install RFID- augmented systems to integrate enterprise information along the life cycle of a product. The new effort demands openness and scalability, and leads to a new paradigm of collaboration using all these data sources. The collaboration requires a metadata technology (for reconciling different data semantics) that works on thin computing environments (e.g., emerging sensor nodes and RFID chips) as well as on traditional databases. It also needs a new extended global query model that supports participants to offer/publish information as they see fit, not just request/subscribe what they want. This paper develops new results toward meeting these requirements.     

Integration of information systems: assessing its quality

Due to organizational and technological changes the need for integrating information systems within healthcare institutions, has increased enormously. Although the technical means for systems integration have definitely matured, integration methodologies are still in their infancy. Two important questions regarding systems integration are hardly ever addressed in a systematic way: how to derive integration requirements, and how to check whether the requirements are met in a given integrated system. These two questions must be answered if we want to assess or improve the quality of integration of a given set of systems. In this article we present a nine-step method for deriving integration requirements from a business process model, and we assess the quality of integration of a given integrated system against these requirements. The method is demonstrated by elaborating two case studies from the health care domain.     

Towards multi-agency sensor information integration for disaster management

Having access to real-time spatial information is central to the functioning of disaster management, and in particular disaster response. Existing spatially-enabled solutions for managing urban disasters provide limited support for time-sensitivity and urgency underlying emergency situations. These approaches mainly suffer from low temporal resolution and inability to source a broad range of required disaster data, together with insufficient support for automated operations. However, disaster management procedures, integrated with in situ sensing, promise an extensive range of real-time data and automated processes to acquire and manage disaster information. In this research, we study the process of integrating multi-agency in situ sensors for supporting disaster management. For this purpose, the research was adopted in Australia as the case study area in disaster management of a flood by emphasizing on the response phase. This paper first identifies the issues and existing requirements in the process of multi-agency sensor information integration and then proposes a standard-based approach to overcoming these integration issues. Afterward, based on the presented approach and identified requirements, a GIS-based software IDDSS-Sensor is implemented to provide the functions of standard-based access, as well as on-the-fly harmonization, integration and usage of multi-agency sensor information. We evaluate the applicability of our developed approach by applying it to the use case of supporting flash flood evacuation response.     

Verification framework and algorithms for integrating information distribution systems

When two competitive companies merge into one bigger company, reusing existing technical resources in each company to form a common technology becomes a priority integration task. One of the specific problems occurring during integration is the resulting integrated scopes' requirements specifications become faulty while integrating two sets of software systems from two participating companies. The integrated scopes refer to the domains of information software systems, business policies, business processes, business rules, interface functions, and data that are being integrated in each participating company.Using a Transition-Directed Graph (TDG) representation, specified requirements involved in the integration will be represented in a form of TDG to be analyzed for faults. Five efficient algorithms are developed to identify faults in the resulting TDG formatted requirement specifications. Four correction algorithms are also developed to correct detected faults found in the TDG formatted requirements specifications.     

针对传感节点的基于signal-slot 电源管理方案设计与实现

随着传感网络和3G网络的融合,物联网已经成为新世纪最重要的技术之一,如何延长传感节点的工作时间已成为物联网研究的一个重要课题。传统的电源管理规范如APM(Advanced Power Management)和ACPI(Advanced Configuration and Power Interface)主要针对PC设计,因其复杂性和对BIOS层要求等因素,在无线传感节点中并不适用。为了解决此问题,针对传感节点计算和存储能力有限的特点,我们首先开发了精简的signalslot框架,基于signal-slot框架,并设计了简单有效的电源管理方案SPM(Simple Power Management),并将SPM在流行的传感节点操作系统Contiki中实现。     

Providing advanced remote medical treatment services through pervasive environments

Advances on sensor technology, wireless environments and data mining introduce new possibilities in the healthcare sector, realizing the anytime-anywhere access to medical information. Towards this direction, integration of packet-switched networks and sensor devices can be effective in deploying assistive environments, such as home monitoring for elderly or patients. In this paper we describe a policy-based architecture that utilizes wireless sensor devices, advanced network topologies and software agents to enable remote monitoring of patients and elderly people; through the aforementioned technologies we achieve continuous monitoring of a patient’s condition and we can proceed when necessary with proper actions. We also present a software framework and network architecture that realizes the provision of remote medical services, in compliance with the imposed security and privacy requirements. A proof of concept prototype is also deployed, along with an evaluation of the overall architecture’s performance.     

物联网环境下多智能体决策信息支持技术

随着物联网技术的不断发展,传感器网络得到了广泛的应用并成为信息技术领域重要的基础设施。尤其是传感网络提供的实时感知信息,为许多智能应用提供了充分的信息支持和必要的决策依据。然而,由于智能应用的实时感知信息需求通常无法转化为简单的查询请求与传感器底层查询接口准确匹配,因此,基于物联网的智能决策常常无法准确获取到决策相关的实时信息。针对此问题,提出一个基于语义覆盖网的物联网信息资源描述、推理和应用模型,并以多智能体系统决策支持为应用基础,研究了新型物联网环境下的多智能体决策信息支持技术。该技术以基于多智能体系统的团队导向规划的任务分解方法为核心,将复杂任务分解为若干简单子任务,并基于本体推理方法把子任务执行时需要的决策信息转化为精确、完备的传感器信息查询,从而实现从物联网中准确定位具体的传感器并获取相应感知信息的实时决策信息支持机制。     

传感器网络及其数据管理的概念、问题与进展

传感器网络综合了传感器技术、嵌入式计算技术、分布式信息处理技术和无线通信技术,能够协作地实时监测、感知和采集各种环境或监测对象的信息,并对其进行处理,传送到这些信息的用户.传感器网络是计算机科学技术的一个新的研究领域,具有十分广阔的应用前景,引起了学术界和工业界的高度重视.介绍了传感器网络及其数据管理的概念和特点,探讨了传感器网络及其数据管理的研究问题,并综述了传感器网络及其数据管理的研究现状.     

Energy conservation in wireless sensor networks: a rule-based approach

The research reported in this paper addresses the problem of energy conservation in wireless sensor networks (WSNs). It proposes concepts and techniques to extract environmental information that are useful for controlling sensor operations, in order to enable sensor nodes to conserve their energy, and consequently prolong the network lifetime. These concepts and techniques are consolidated in a generic framework we term CASE: Context Awareness in Sensing Environments framework. CASE targets energy conservation at the network level. A subset framework of CASE, we term CASE Compact, targets energy conservation at the sensor node level. In this paper, we elaborate on these two frameworks, elucidate the requirements for them to operate together within a WSN and evaluate the applications they can be applied to for energy conservation.     

Application of iot medical image detection and prenatal genetic testing in obstetric clinic

The use of new techniques and methods can be significantly improved during prenatal examination and screening of fetal genetic abnormalities. Expanding the number of vectors in screening and screening for single-cell gene diseases in fetal DNA will increase the probability of success of the whole screening test.Recently, sub chromosomal abnormalities have been introduced into prenatal care. The lack of suitable tools makes it very difficult to obtain information about collection, management, and prenatal genetic testing. The purpose of this study is to improve the accessibility of nutritional information by using the advantages of advanced medical image detection and integrating the IoT (Internet of Things) and prenatal genetic testing platforms in obstetric outpatient clinics. Records are kept that the platform will allow IoT to interact with sensor practitioners and provide immediate access to medical images prior to delivery. This proposed system provides an IoT application for managing obstetric outpatient information and prenatal genetic testing requirements. The requirement for establishment is the realization of the development of an IoT platform for complete medical image detection information management with IoT input required for integration with existing medical information systems. In response, the purpose of the study is to use integrated IoT applications to help nutrition professionals, researchers, patients, and especially mothers, to enhance their collection of medical imaging and prenatal genetic testing IoT sensor based information transfer.     

Multisensor-based object identification using uncertain geometric models

One of the problems in sensor integration is how to design the integration strategy for the given task. In this paper, we deal with model-based object recognition from uncertain geometric observations using uncertain object models. First, we decompose the recognition problem into a hierarchy of statistically well-defined subproblems depending on sensor uncertainties and model uncertainties. A recognition algorithm based on this approach is developed. Second, a method to preserve the consistency under model uncertainties is discussed. It is shown that information loss can be avoided by adding dummy variables to parameters in the integration. Finally, applications of the proposed method to two-dimensional object recognition are demonstrated.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Automation and decision support in interactive consumer products

This article presents two empirical studies (n = 30, n = 48) that are concerned with different forms of automation in interactive consumer products. The goal of the studies was to evaluate the effectiveness of two types of automation: perceptual augmentation (i.e. supporting users' information acquisition and analysis); and control integration (i.e. supporting users' action selection and implementation). Furthermore, the effectiveness of on-product information (i.e. labels attached to product) in supporting automation design was evaluated. The findings suggested greater benefits for automation in control integration than in perceptual augmentation alone, which may be partly due to the specific requirements of consumer product usage. If employed appropriately, on-product information can be a helpful means of information conveyance. The article discusses the implications of automation design in interactive consumer products while drawing on automation models from the work environment.     

粮仓环境监测系统设计

随着科学技术的发展,粮食行业信息化发展得到了强有力的科技支撑。为满足粮库整体信息化建设的要求,打破"信息孤岛"现状,实现资源整合、数据共享,为大数据分析和数据应用提供数据基础,设计一款能够实时采集粮仓内温湿度、氧气、二氧化碳气体浓度、粮堆高度的数据集成系统。系统采用分层分布式结构,由数据集中器和多种传感器模块组成,数据集中器与各传感器模块间通过RS485总线进行数据通讯,并由数据集中器通过网络专线传输汇总的环境监测数据,通过专用软件进行数据处理和挖掘,从而实现粮仓环境监测和预警等功能。     

Sensor integration and data fusion

The problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that information from various sensors may be available in different forms at the fusion. For example, data from infrared (IR) sensors may be combined with range radar (RR) data and further combined with visual images. In each case, data and information from different sensors are presented in a different format which may not be directly compatible for all sensors. Part of the available information may be in the form of attributes and part in the form of dynamical measurements. A generalized evidence processing theory and an architecture for sensor integration and data fusion that accommodates diversified sources of information are presented. Data (or, more generically, information) fusion may take place at different levels, such as the level of dynamics, the level of attributes, and the level of evidence. The common and different aspects of fusion at the different levels are investigated and several practical examples of real world data fusion problems are discussed.     

汽车尾气用NO_x传感器

汽车尾气中的有害物主要有CO,HC,NOx,SOx以及一些微粒物质,给人类赖以生存的大气环境带来了严重的危害。汽车尾气用NOx传感器开发主要是为了解决NOx(包括NO,NO2)的排放问题,能够实现准确、快速地测定NOx含量,从而满足大气质量检测和环境保护的要求。目前,NOx气体传感器主要有半导体型、电位型和电流型。主要介绍了这3种汽车用NOx传感器的原理、结构及应用,并简要分析了其向微型化、集成化和智能化等方向发展的趋势。     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

面向频道服务的柔性集成模型

针对企业在业务系统集成中对柔性的要求,提出一种信息的集成逻辑组织单元——频道和集成服务组件框架模型,采用总线的消息传输技术和可插入式的构件技术,通过配置不同频道服务实现从接入到消息总线上的消息服务的动态注册和服务提供,部署集成服务组件可扩展频道的集成服务功能。在交通信息集成的应用过程中证明该模型易于扩展并具有较好的柔性。     

基于敏捷制造环境的企业集成信息系统模型研究

首先分析了敏捷制造环境下企业集成信息系统的功能要求 ;然后提出了实现企业信息集成的系统模型 ,该模型由企业内部管理信息系统、企业网站、外部敏捷制造环境三部分组成 ,利用该模型可实现企业内外信息的集成 ,从而达到提高企业敏捷性的目的 ;最后给出了实现企业集成信息系统的主要技术。